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Fire spread predictions: Sweeping uncertainty under the rug

Benali, Akli ; Sá, Ana C.L. ; Ervilha, Ana R. ; Trigo, Ricardo M. ; Fernandes, Paulo M. ; Pereira, José M.C.

The Science of the total environment, 2017-08, Vol.592, p.187-196 [Periódico revisado por pares]

Netherlands: Elsevier B.V

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  • Título:
    Fire spread predictions: Sweeping uncertainty under the rug
  • Autor: Benali, Akli ; Sá, Ana C.L. ; Ervilha, Ana R. ; Trigo, Ricardo M. ; Fernandes, Paulo M. ; Pereira, José M.C.
  • Assuntos: FARSITE ; Generalized Likelihood Uncertainty Estimation (GLUE) ; Hotspots ; Likelihood ; MODIS ; Satellite
  • É parte de: The Science of the total environment, 2017-08, Vol.592, p.187-196
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
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  • Descrição: Predicting fire spread and behavior correctly is crucial to minimize the dramatic consequences of wildfires. However, our capability of accurately predicting fire spread is still very limited, undermining the utility of such simulations to support decision-making. Improving fire spread predictions for fire management purposes, by using higher quality input data or enhanced models, can be expensive, unfeasible or even impossible. Fire managers would benefit from fast and inexpensive ways of improving their decision-making. In the present work, we focus on i) understanding if fire spread predictions can be improved through model parameter calibration based on information collected from a set of large historical wildfires in Portugal; and ii) understanding to what extent decreasing parametric uncertainty can counterbalance the impact of input data uncertainty. Our results obtained with the Fire Area Simulator (FARSITE) modeling system show that fire spread predictions can be continuously improved by ‘learning’ from past wildfires. The uncertainty contained in the major input variables (wind speed and direction, ignition location and fuel models) can be ‘swept under the rug’ through the use of more appropriate parameter sets. The proposed framework has a large potential to improve future fire spread predictions, increasing their reliability and usefulness to support fire management and decision making processes, thus potentially reducing the negative impacts of wildfires. [Display omitted] •Uncertainties undermine the utility of fire spread predictions.•Model parameter calibration was made using the GLUE methodology.•Prediction accuracy was estimated using satellite active fire data.•The impact of uncertainty was reduced, improving prediction accuracy.•Large potential to improve future fire spread predictions.
  • Editor: Netherlands: Elsevier B.V
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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